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업데이트 내용은 다음과 같습니다.

1. 기존에 작업하던 1공구 SP-11의 전체단계(4단계)에 대하여 반복문을 통하여 돌아갈 수 있도록 수정하였습니다. 
  (1_SP-11_Test.csv/ main_Rev.4.py / main.Rev.4_svg)
  
2. 앞서 완성한 main_Rev.4.py 코드의 적용성 검토를 위하여 
   3공구 SP-68 지점 데이터(4단계) 적용한 결과 첨부해드립니다.
   (3_SP-68_Test.csv / 3_SP-68_Rev.4.svg)
   
+) 지점 데이터에 변화에 대한 입력값 수정 필요 부분은 다음과 같습니다.
  line 50 : 불러올 .csv 파일명 
  line 61-62 : start/end index
  line 64 : 성토 단계 
  line 170 ~ : 그래프 추가 
  line 183 : .svg 파일명

1_SP-11_Test.csv

@@ -0,0 +1,125 @@
+Time,Settle,Surcharge
+0,0,1.5
+5,17.4,1.5
+7,23.9,1.5
+11,32.2,1.5
+14,41.7,1.5
+21,64.1,1.5
+28,72.5,1.5
+35,78.8,1.5
+42,93.3,1.5
+48,102.5,1.5
+53,108,3.002
+54,109.2,3.002
+55,110.4,3.002
+56,111.6,3.002
+59,117.3,3.002
+60,119.2,3.002
+61,121.1,3.002
+62,122.7,3.002
+67,130.2,3.002
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+69,133.6,3.002
+70,135.4,3.002
+74,141.4,3.002
+75,142.9,3.002
+76,144.4,3.002
+77,146.2,3.002
+80,149.2,3.002
+81,150.2,3.002
+82,151.2,3.002
+83,152.2,3.002
+91,162.8,3.002
+98,170,3.002
+105,177,3.002
+112,182.4,3.002
+115,185,3.002
+117,186.5,3.002
+118,187.3,3.002
+122,202.9,4.095
+124,210.5,4.095
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+286,358,4.095
+294,360.9,4.095
+300,363,5.256
+301,363.4,5.256
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+306,367.2,5.256
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+398,405.3,5.256
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+434,413.3,5.256
+440,414.5,5.256
+447,415.9,5.256
+455,417.5,5.256
+461,418.7,5.256
+468,420,5.256

3_SP-68_Test.csv

@@ -0,0 +1,128 @@
+Time,Settle,Surcharge
+0,0,1.887
+3,41,1.887
+6,61.5,1.887
+10,73.1,1.887
+13,81.6,1.887
+17,86.9,1.887
+20,91.9,1.887
+23,96.7,1.887
+26,101.6,1.887
+30,107.7,2.94
+33,113,2.94
+37,119.4,2.94
+40,122.8,2.94
+44,128.4,2.94
+47,131.9,2.94
+52,139.5,2.94
+54,140.5,2.94
+59,148,2.94
+62,151,2.94
+65,153.8,2.94
+68,156.6,2.94
+72,160,2.94
+75,162.5,2.94
+79,166,2.94
+83,169.1,2.94
+86,172,2.94
+89,174.7,2.94
+94,180.3,2.94
+96,182.1,2.94
+100,185.5,2.94
+103,188.4,2.94
+110,195.8,2.94
+114,198.7,2.94
+118,201.1,2.94
+122,205,2.94
+125,206.9,2.94
+128,209.3,2.94
+131,211.8,2.94
+135,214.2,2.94
+138,216.2,2.94
+143,217.4,2.94
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+150,219.1,2.94
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+159,222,2.94
+164,227.7,2.94
+167,230.7,2.94
+171,234.8,3.48
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+299,300.6,3.48
+303,302.2,3.48
+306,303.5,3.48
+310,312.8,5.608
+313,318.1,5.608
+317,322.8,6.794
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+324,335.3,6.794
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+402,389.9,6.794
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+412,394,6.794
+415,396.7,6.794
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+425,399.1,6.794
+429,399.8,6.794
+432,400.9,6.794
+436,403.6,6.794
+439,405.6,6.794
+443,408.2,6.794
+446,409.4,6.794

main_Rev.4.py

@@ -0,0 +1,184 @@
+# =================
+# Import 섹션
+# =================
+
+import numpy as np
+from scipy.optimize import least_squares
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+import pandas as pd
+
+# =================
+# Function 섹션
+# =================
+
+# 주어진 계수를 이용하여 쌍곡선 시간-침하 곡선 반환
+def generate_data_hyper(px, pt):
+    return pt / (px[0] * pt + px[1])
+
+# 회귀식과 측정치와의 잔차 반환 (비선형 쌍곡선)
+def fun_hyper_nonlinear(px, pt, py):
+    return pt / (px[0] * pt + px[1]) - py
+
+# =================
+# Step별 활용 Function
+# =================
+
+# i단계 보정 침하량 산정
+def fun_step_measured_correction(m, p):
+    return m - p
+
+# i단계 t-ti 산정
+def fun_step_time_correction(t, ti):
+    return t - ti
+
+# i단계 침하곡선 작성
+def settlement_prediction_curve(m1, p1):
+    return m1 + p1
+
+# i단계 보정 예측 침하량 산정
+def fun_step_prediction_correction(m2, p2):
+    return p2 + (m2[0] - p2[0])
+
+
+
+# =================
+# 입력값 설정
+# =================
+
+# CSV 파일 읽기
+data = pd.read_csv("3_SP-68_Test.csv")
+
+# 시간, 침하량, 성토고 배열 생성
+time = data['Time'].to_numpy()
+settle = data['Settle'].to_numpy()
+surcharge = data['Surcharge'].to_numpy()
+
+# =================
+# 성토 단계 구분
+# =================
+
+step_start_index = [0, 9, 49, 90] # 단계별 성토 시작 지점 입력(4단계 이므로 4개)
+step_end_index = [8, 48, 89, 129] # 단계별 성토 종료 지점 입력(4단계 이므로 4개)
+x0 = np.ones(2)
+num_step = 4
+
+for i in range(0, num_step): # 성토 단계에 따라 수정(4단계 이므로 0~4)
+
+    # i단계 실측 기간 및 침하량
+    globals()['tm_{}'.format(i)] = time[step_start_index[i]:step_end_index[i]]
+    globals()['ym_{}'.format(i)] = settle[step_start_index[i]:step_end_index[i]]
+
+    if i == 0 : # 1단계
+
+        res_lsq_hyper_nonlinear_0 = least_squares(fun_hyper_nonlinear, x0, args=(tm_0, ym_0))
+        print(res_lsq_hyper_nonlinear_0.x)
+
+        globals()['settle_predicted_{}'.format(i)] = generate_data_hyper(res_lsq_hyper_nonlinear_0.x, time)
+
+    elif 0 < i < (num_step - 1):
+
+        # i단계~최종 실측 기간 및 침하량
+        globals()['tmm_{}'.format(i)] = time[step_start_index[i]:step_end_index[-1]]
+        globals()['ymm_{}'.format(i)] = settle[step_start_index[i]:step_end_index[-1]]
+
+        # i-1단계 예측 침하량(i단계 기간에 해당하는)
+        globals()['yp_{}'.format(i)] = globals()['settle_predicted_{}'.format(i - 1)][(step_start_index[i]-step_start_index[i-1]):(step_end_index[i]-step_start_index[i-1])]
+        # i-1 단계 예측 침하량 (i단계~최종)
+        globals()['ypp_{}'.format(i)] = globals()['settle_predicted_{}'.format(i - 1)][(step_start_index[i]-step_start_index[i-1]):(step_end_index[-1]-step_start_index[i-1])]
+
+        # i단계 실측 보정 침하량 산정
+        globals()['step_{}_measured_correction'.format(i)] = fun_step_measured_correction(globals()['ym_{}'.format(i)],globals()['yp_{}'.format(i)])
+        # i단계 t-ti 산정
+        globals()['step_{}_time_correction'.format(i)] = fun_step_time_correction(globals()['tmm_{}'.format(i)],
+                                                                                  globals()['tm_{}'.format(i)][0])
+
+        # i 단계 보정 침하량에 대한 예측 침하량 산정
+        globals()['res_lsq_hyper_nonlinear_{}'.format(i)] = least_squares(fun_hyper_nonlinear, x0,
+                                          args=(globals()['step_{}_time_correction'.format(i)][0:(step_end_index[i]-step_start_index[i])],
+                                                globals()['step_{}_measured_correction'.format(i)]))
+
+
+        print(globals()['res_lsq_hyper_nonlinear_{}'.format(i)].x)
+
+        globals()['settle_hyper_nonlinear_{}'.format(i)] = generate_data_hyper(globals()['res_lsq_hyper_nonlinear_{}'.format(i)].x,
+                                                                               globals()['step_{}_time_correction'.format(i)])
+
+        # i단계 침하곡선 작성
+        globals()['step_{}_prediction_curve'.format(i)] = settlement_prediction_curve(globals()['settle_hyper_nonlinear_{}'.format(i)],
+                                                                                      globals()['ypp_{}'.format(i)])
+
+        # i단계 보정 예측 침하량 산정
+        globals()['settle_predicted_{}'.format(i)] = fun_step_prediction_correction(globals()['ymm_{}'.format(i)],
+                                                                                    globals()['step_{}_prediction_curve'.format(i)])
+
+
+    else: # 최종 성토 단계
+
+        # i-1 단계 예측 침하량 (최종 단계에 해당하는)
+        globals()['yp_{}'.format(i)] = globals()['settle_predicted_{}'.format(i - 1)][(step_start_index[i]-step_start_index[i-1]):step_end_index[i]]
+
+        # 최종 단계 실측 보정 침하량 산정
+        globals()['step_{}_measured_correction'.format(i)] = fun_step_measured_correction(globals()['ym_{}'.format(i)],
+                                                                                          globals()['yp_{}'.format(i)])
+
+        # 최종 단계 t-ti 산정
+        globals()['step_{}_time_correction'.format(i)] = fun_step_time_correction(globals()['tm_{}'.format(i)],
+                                                                                  globals()['tm_{}'.format(i)][0])
+
+        # 최종 단계 보정 침하량에 대한 예측 침하량 산정
+        globals()['res_lsq_hyper_nonlinear_{}'.format(i)] = least_squares(fun_hyper_nonlinear, x0,
+                                                             args=(globals()['step_{}_time_correction'.format(i)],
+                                                              globals()['step_{}_measured_correction'.format(i)]))
+
+        print(globals()['res_lsq_hyper_nonlinear_{}'.format(i)].x)
+
+        globals()['settle_hyper_nonlinear_{}'.format(i)] = generate_data_hyper(globals()['res_lsq_hyper_nonlinear_{}'.format(i)].x,
+                                                                               globals()['step_{}_time_correction'.format(i)])
+
+        # 최종 단계 침하곡선 작성
+        globals()['step_{}_prediction_curve'.format(i)] = settlement_prediction_curve(globals()['settle_hyper_nonlinear_{}'.format(i)],
+                                                                                      globals()['yp_{}'.format(i)])
+
+        # 최종단계 보정 예측 침하량 산정
+        globals()['settle_predicted_{}'.format(i)] = fun_step_prediction_correction(globals()['ym_{}'.format(i)],
+                                                                                    globals()['step_{}_prediction_curve'.format(i)])
+
+'''
+나중에: 그래프 작성
+'''
+
+# 그래프 크기, 서브 그래프 개수 및 비율 설정
+f, axes = plt.subplots(2,1, figsize=(10, 10),
+                         gridspec_kw={'height_ratios':[1,2]})
+
+# 성토고 그래프 표시
+axes[0].plot(time, surcharge, color='black', label='surcharge height')
+
+axes[0].set_ylabel("Surcharge height (m)", fontsize = 17)
+axes[0].set_xlim(left = 0)
+axes[0].grid(color="gray", alpha=.5, linestyle='--')
+axes[0].tick_params(direction='in')
+
+# 계측 침하량 표시
+axes[1].scatter(time, -settle, s = 50, facecolors='white', edgecolors='black', label = 'measured data')
+
+# 예측 침하량 표시
+axes[1].plot(time, -settle_predicted_0, linestyle='--', color='red', label='Predicted Curve_Step 1')
+axes[1].plot(tmm_1, -settle_predicted_1, linestyle='--', color='blue', label='Predicted Curve_Step 2')
+axes[1].plot(tmm_2, -settle_predicted_2, linestyle='--', color='green', label='Predicted Curve_Step 3')
+axes[1].plot(tm_3, -settle_predicted_3, linestyle='--', color='orange', label='Predicted Curve_Step 4')
+
+# 예측 침하량 그래프 설정
+axes[1].set_xlabel("Time (day)", fontsize = 17)
+axes[1].set_ylabel("Settlement (mm)", fontsize = 17)
+axes[1].set_ylim(top = 0)
+axes[1].set_ylim(bottom = -1.5 * settle.max())
+axes[1].set_xlim(left = 0)
+
+# 범례 표시
+axes[1].legend(loc=1, ncol=2, frameon=True, fontsize=12)
+
+# 그래프 저장 및 출력
+plt.savefig('3_SP-68_Rev.4_Test.svg', dpi=300)
+plt.show()